IKVAV functionalized oriented PCL/Fe3O4 scaffolds for magnetically modulating DRG growth behavior.

The re-bridging of the deficient nerve is the main problem to be solved after the functional impairment of the peripheral nerve. In this study, a directionally aligned polycaprolactone/triiron tetraoxide (PCL/Fe3O4) fiber scaffolds were firstly prepared by electrospinning technique, and further then grafted with IKVAV peptide for regulating DRG growth and axon extension in peripheral nerve regeneration. The results showed that oriented aligned magnetic PCL/Fe3O4 composite scaffolds were successfully prepared by electrospinning technique and possessed good mechanical properties and magnetic responsiveness. The PCL/Fe3O4 scaffolds containing different Fe3O4 concentrations were free of cytotoxicity, indicating the good biocompatibility and low cytotoxicity of the scaffolds. The IKVAV-functionalized PCL/Fe3O4 scaffolds were able to guide and promote the directional extension of axons, the application of external magnetic field and the grafting of IKVAV peptides significantly further promoted the growth of DRGs and axons. The ELISA test results showed that the AP-10 F group scaffolds promoted the secretion of nerve growth factor (NGF) from DRG under a static magnetic field (SMF), thus promoting the growth and extension of axons. Importantly, the IKVAV-functionalized PCL/Fe3O4 scaffolds could significantly up-regulate the expression of Cntn2, PCNA, Sox10 and Isca1 genes related to adhesion, proliferation and magnetic receptor function under the stimulation of SMF. Therefore, IKVAV-functionalized PCL/Fe3O4 composite oriented scaffolds have potential applications in neural tissue engineering.

Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.

OBJECTIVE: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data. MATERIALS AND METHODS: We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i.e., main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. RESULTS: Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The "integrated" imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies. CONCLUSION: The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.

Potential Effects of Metal Oxides on Agricultural Production of Rice: A Mini Review.

The extensive usage of metal oxide nanoparticles has aided in the spread and accumulation of these nanoparticles in the environment, potentially endangering both human health and the agroecological system. This research describes in detail the hazardous and advantageous impacts of common metal oxide nanomaterials, such as iron oxide, copper oxide, and zinc oxide, on the life cycle of rice. In-depth analyses are conducted on the transport patterns of nanoparticles in rice, the plant's reaction to stress, the reduction of heavy metal stress, and the improvement of rice quality by metal oxide nanoparticles, all of which are of significant interest in this subject. It is emphasized that from the perspective of advancing the field of nanoagriculture, the next stage of research should focus more on the molecular mechanisms of the effects of metal oxide nanoparticles on rice and the effects of combined use with other biological media. The limitations of the lack of existing studies on the effects of metal oxide nanomaterials on the entire life cycle of rice have been clearly pointed out.

Arterial spin labeling and diffusion-weighted imaging for identification of retropharyngeal lymph nodes in patients with nasopharyngeal carcinoma.

BACKGROUND: To evaluate the parameters derived from arterial spin labeling (ASL) and multi-b-value diffusion-weighted imaging (DWI) for differentiating retropharyngeal lymph nodes (RLNs) in patients with nasopharyngeal carcinoma (NPC). METHODS: This prospective study included 50 newly diagnosed NPC and 23 healthy control (HC) participants. RLNs of NPC were diagnosed according to the follow-up MRI after radiotherapy. Parameters derived from ASL and multi-b-value DWI, and RLNs axial size on pre-treatment MRI among groups were compared. Receiver operating characteristic curve (ROC) was used to analyze the diagnostic efficiency. RESULTS: A total of 133 RLNs were collected and divided into a metastatic group (n = 71) and two non-metastatic groups (n = 62, including 29 nodes from NPC and 33 nodes from HC). The axial size, blood flow (BF), and apparent diffusion coefficient (ADC) of RLNs were significantly different between the metastasis and the non-metastasis group. For NPC patients with a short axis < 5 mm or < 6 mm, or long axis < 7 mm, if BF > 54 mL/min/100 g or ADC ≤ 0.95 × 10-3 mm2/s, the RLNs were still considered metastatic. Compared with the index alone, a combination of size and functional parameters could improve the accuracy significantly, except the long axis combined with ADC; especially, combined size with BF exhibited better performance with an accuracy of 91.00-92.00%. CONCLUSIONS: ASL and multi-b-value DWI could help determine the N stage of NPC, while the BF combination with RLNs size may significantly improve the diagnostic efficiency.

Study on Forming Law and Penetration of a Spherical Cone Composite Structure Liner Based on the Explosion Pressure-Coupling Constraint Principle.

The liner is an important part of shaped charge. In this paper, the spherical cone composite structure liner composed of a spherical missing body and truncated cone (hereinafter referred to as the SCS liner) is studied. The SCS liner is made of copper. Based on this, a shaped charge structure based on the explosion pressure-coupling constraint principle is designed, filling an 8701 explosive (RDX-based explosive). Through pulse X-ray tests, numerical simulation, and static explosion tests, the significance of the detonation pressure-coupling constraint principle, as well as the forming law and penetration efficiency of the SCS liner are studied. The results show that in the pulsed X-ray test, a split jet with high velocity is formed in the SCS liner. The explosion pressure-coupling constraint principle delays the attenuation of the internal explosion pressure and improves the shape of jet. After the SCS liner is selected, the penetration depth is increased by 70.38%. The average head velocity of the explosive charge jet is 7594.81 m/s. The diameter of the hole formed by the jet of the explosive charge is 20.33 mm. The hole expands inside, and the perforation depth is 178.87 mm. The numerical simulation is in good agreement with the test.

Investigating the value of arterial spin labeling and intravoxel incoherent motion imaging on diagnosing nasopharyngeal carcinoma in T1 stage.

BACKGROUND: To investigate the diagnostic value of arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM) imaging in distinguishing nasopharyngeal carcinoma (NPC) in T1 stage from healthy controls (HC). METHODS: Forty-five newly diagnosed NPC patients in the T1 stage and thirty-one healthy volunteers who underwent MR examinations for both 3D pseudo-continuous ASL (pCASL) and IVIM were enrolled in this study. The Mann-Whitney test was used to compare the mean values of blood flow (BF) derived from pCASL and IVIM derived parameters, including apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*) and perfusion fraction (f) between NPC tumor and benign nasopharyngeal mucosa of HC. Receiver Operating Characteristic (ROC) was performed to determine diagnostic cutoff and efficiency. The correlation coefficients among parameters were investigated using Spearman's test. RESULTS: The NPC in the T1 stage showed higher mean BF, lower ADC, D, and f compared to benign nasopharyngeal mucosa (P < 0.001) with the area under curve of ROC of 0.742-0.996 (highest by BF). BF cutoff was set at > 36 mL/100 g/min; the corresponding sensitivity, specificity, and accuracy in differentiating NPC stage T1 from benign nasopharyngeal mucosa were 95.56% (43/45), 100% (31/31) and 97.37% (74/76), respectively. BF demonstrated moderate negative correlation with D* on HC (ρ [Spearman correlation coefficients] = - 0.426, P = 0.017). CONCLUSIONS: ASL and IVIM could reflect the difference in perfusion and diffusion between tumor and benign nasopharyngeal mucosa, indicating a potential for accessing early diagnosis of NPC. Notably, BF, with a specificity of 100%, demonstrated better performance compared to IVIM in distinguishing malignant lesions from healthy tissue.

Differentiation of endometrial adenocarcinoma from adenocarcinoma of cervix using kinetic parameters derived from DCE-MRI.

PURPOSE: This prospective study aimed to investigate the value of kinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating uterine endometrioid adenocarcinoma (EAC) from adenocarcinoma of cervix (AdC). METHODS: Seventy-five newly diagnosed patients with distinctive pathology underwent DCE-MRI. Observers independently calculated the tumor diameters and DCE-MRI parameters using both population and individual-based arterial input function (AIF). Inter-observer consistency was evaluated, and a comparative analysis between EAC (n = 47) and AdC (n = 28) was performed. Regression analysis was used to select parameters that best distinguished EAC from AdC, and to generate predictive models. Receiver operating characteristic curve (ROC) was applied to calculate the diagnostic efficiency of single parameter and the predictive models. RESULTS: Inter-observer consistency was excellent (intra-class correlation [ICC] = 0.902-0.981), especially when calculated via population AIF with relatively higher ICC and smaller SD on Bland-Altman plot. Tumor diameters were not correlated with tumor types. All the DCE-MRI parameters were lower in EAC compared to AdC, except Kep by population AIF and TTP by both sets of AIFs. The statistical parameters were Ve, Maxslop, and Maxconc by population AIF, and Maxslop and Ktrans by individual AIF included in the predictive models, respectively. The two predictive models with combined parameters showed improved diagnostic efficiency in differentiating these two diseases compared with a single parameter. CONCLUSION: DCE-MRI can quantitatively evaluate the perfusion difference between EAC and AdC, thus improving the identification of uterine adenocarcinoma with uncertain biopsy pathology.

[Responses of soil respiration to temperature under different mulching modes in a dryland corn field]. / 不同覆盖方式下旱作玉米田土壤呼吸对温度变化的响应.

Based on a 9-year field experiment, soil samples under straw mulching (SM), plastic film mulching (FM) and no mulching (CK) were incubated at 15, 25 and 35 ℃ for 60 d to investigate the responses of soil respiration to warming and its temperature sensitivity. The results showed that during the whole incubation period, soil respiration rate exhibited a unimodal distribution, while the cumulative soil respiration increased with an "S" curve. The cumulative soil respiration during the first 30 d accounted for about 75%-85% of total during the whole incubation period. The cumulative SM increased by 19.4% compared with CK, whereas no difference was detected between CK and FM. At 25 ℃ and 35 ℃, the mean soil respiration rate increased by 17.0% and 36.8%, and the cumulative CO2 release of soil respiration increased by 13.1% and 33.6%, respectively, compared with 15 ℃. No interaction was detected between mulching method and temperature. 97.7%-99.9% of variation in soil respiration could be explained by temperature change, with soil respiration being positively correlated with organic carbon and total nitrogen content. Compared with no mulching and plastic film mulching, straw mulching could significantly promote soil respiration by increasing the input of organic matter in the soil, but reduced the temperature sensitivity of soil respiration. Straw mulching rather than plastic film mulching would be more efficient at reducing CO2 emission in the Loess Plateau dryland farming area under the context of global warming.

MRI texture analysis based on 3D tumor measurement reflects the IDH1 mutations in gliomas - A preliminary study.

OBJECTIVE: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region. MATERIALS AND METHODS: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated. RESULTS: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable. CONCLUSION: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas.

Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement.

PURPOSE: To evaluate the application of conventional MRI histogram analysis based on the whole tumor measurement on assessing meningioma grading. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board. A total amount of 90 patients with meningioma were enrolled and the preoperative MRI of them were analyzed. To be specific, the patient group were consisted of 45 patients with grade I, 38 with grade II, and 7 with grade III meningioma. Grade I meningioma is classified as low grade meningioma (LGM), whereas Grade II and III meningioma were combined and classified as high grade meningioma (HGM). ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI. 3D ROI signal intensity histogram and all its parameters were obtained. Independent t-test and Kruskal-Wallis test were used for comparison between two groups. Univariate logistic regression analysis and Spearman's correlation analysis were used to screen for the parameters with high predictive efficiency, while multivariate logistic regression analysis was used to determine the optimal model for the classification of meningioma. RESULTS: There were significant differences observed between HGM and LGM groups regarding to histogram volume count, uniformity of three sequences, range of T1WI and T2WI, kurtosis, standard deviation, variance, max intensity of T2WI, skewness, mean deviation, minimum intensity, mean value, the 5th percentile, the 10th percentile, the 25th percentile, the 50th percentile, the 75th percentile, and the 90th percentile of contrasted T1WI. Volume count and uniformity were high predictive parameters in distinguishing HGM from LGM. Logistic regression model included contrasted T1WI histogram parameters (i.e. minimum intensity, volume count, skewness, uniformity, and the 75th percentile) showed the best diagnostic efficiency for meningioma grade, with a sensitivity and specificity of 83.9% and 77.4% (AUC = 0.834, cutoff value = 0.413), respectively. The optimal model was achieved with a sensitivity of 71.4% and a specificity of 78.6% in the test set (AUC = 0.791, cutoff value = 0.413). CONCLUSIONS: Histogram analysis of conventional MRI based on 3D tumor measurement can be applied in the assessment of meningioma grading in clinical.

Actin Dynamics Couples Extracellular Signals to the Mobility and Molecular Stability of Telomeres.

Genome regulatory programs such as telomere functioning require extracellular signals to be transmitted from the microenvironment to the nucleus and chromatin. Although the cytoskeleton has been shown to directly transmit stresses, we show that the intrinsically dynamic nature of the actin cytoskeleton is important in relaying extracellular signals to telomeres. Interestingly, this mechanical pathway not only transmits physical stimuli but also chemical stimuli. The cytoskeletal network continuously reorganizes and applies dynamic forces on the nucleus and feeds into the regulation of telomere dynamics. We further found that distal telomeres are mechanically coupled in a length- and timescale-dependent manner and identified nesprin 2G as well as lamin A/C as being essential to regulate their translational dynamics. Finally, we demonstrated that such mechanotransduction events impinge on the binding dynamics of critical telomere binding proteins. Our results highlight an overarching physical pathway that regulates positional and molecular stability of telomeres.

Intrinsic frequency specific brain networks for identification of MCI individuals using resting-state fMRI.

Numerous brain oscillations are well organized into several brain rhythms to support complex brain activities within distinct frequency bands. These rhythms temporally coexist in the same or different brain areas and may interact with each other with specific properties and physiological functions. However, the identification and evaluation of these various brain rhythms derived from BOLD-fMRI signals are obscure. To address this issue, we introduced a data-driven method named Complementary Ensemble Empirical Mode Decomposition (CEEMD) to automatically decompose the BOLD oscillations into several brain rhythms within distinct frequency bands. Thereafter, in order to evaluate the performance of CEEMD in the detection of subtle BOLD signals, a novel CEEMD-based high-dimensional pattern classification framework was proposed to accurately identify mild cognitive impairment individuals from the healthy controls. Our results showed CEEMD is a stable frequency decomposition method. Furthermore, CEEMD-based frequency specific topological profiles provided a classification accuracy of 93.33%, which was saliently higher than that of the conventional frequency separation based scheme. Importantly, our findings demonstrated that CEEMD could provide an effective means for brain oscillation separation, by which a more meaningful frequency bins could be used to detect the subtle changes embedded in the BOLD signals.

Frequency specific brain networks in Parkinson's disease and comorbid depression.

The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson's disease (DPD, n = 20) , non-depressed Parkinson's disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson's disease.

Frequency dependent topological patterns of resting-state brain networks.

The topological organization underlying brain networks has been extensively investigated using resting-state fMRI, focusing on the low frequency band from 0.01 to 0.1 Hz. However, the frequency specificities regarding the corresponding brain networks remain largely unclear. In the current study, a data-driven method named complementary ensemble empirical mode decomposition (CEEMD) was introduced to separate the time series of each voxel into several intrinsic oscillation rhythms with distinct frequency bands. Our data indicated that the whole brain BOLD signals could be automatically divided into five specific frequency bands. After applying the CEEMD method, the topological patterns of these five temporally correlated networks were analyzed. The results showed that global topological properties, including the network weighted degree, network efficiency, mean characteristic path length and clustering coefficient, were observed to be most prominent in the ultra-low frequency bands from 0 to 0.015 Hz. Moreover, the saliency of small-world architecture demonstrated frequency-density dependency. Compared to the empirical mode decomposition method (EMD), CEEMD could effectively eliminate the mode-mixing effects. Additionally, the robustness of CEEMD was validated by the similar results derived from a split-half analysis and a conventional frequency division method using the rectangular window band-pass filter. Our findings suggest that CEEMD is a more effective method for extracting the intrinsic oscillation rhythms embedded in the BOLD signals than EMD. The application of CEEMD in fMRI data analysis will provide in-depth insight in investigations of frequency specific topological patterns of the dynamic brain networks.